Aircraft and automakers are concerned with manufacturing highly complex systems. Aircrafts and cars have to be manufactured to a multitude of strict security and safety regulations.
Components of aircraft are complex and expensive systems in their own rights. Therefore, in order for industry to operate at acceptable margins, manufacturing processes for those components need to be highly efficient to ensure high output that nevertheless complies with those standards.
Development and testing are crucial stages in the manufacturing pipeline binding considerable resources. In many cases, the data on which testing and development of the components is based is available as a large number of samples of high-dimensional, computerized mesh geometries representing shape and forms or other qualities or parameters of the components to be manufactured or tested.
The mesh geometries comprise a large number of data points in spatial or higher dimensional coordinates. The mesh geometries are obtained for example from real-life crash-tests or from tests implemented as computer simulations. The mesh geometries so obtained are subject to statistical variations.
There is an issue in prior art development and testing systems in that those statistical variations are represented and acquired only in a point-wise manner across the totality of data points in the mesh geometries. Prior art systems allow for example the representation of the variation as a single value or as a pair of maximum and minimum value in relation to only one data point at a time across the plurality of mesh geometries. The statistical variations across all the mesh geometries each taken as a whole hitherto defies acquisition and representation by current systems.
There is therefore a need in the art to address the issues identified above.